JP7635211B2 - Systems and methods for continuous therapy - Patents.com - Google Patents

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of and priority to U.S. Provisional Patent Application No. 62/899,833, filed September 13, 2019, the entirety of which is incorporated herein by reference.

The present disclosure relates generally to systems and methods for determining sleep-related parameters of a user during a sleep session, and more particularly to systems and methods for determining two or more sets of sleep-related parameters of a user during a sleep session using two or more separate and distinct sensors.

Many people suffer from sleep-related breathing disorders associated with one or more events occurring during sleep, such as, for example, snoring, apnea, hypopnea, restless legs, sleep disturbances, choking, increased heart rate, dyspnea, asthma attacks, epilepsy episodes, seizures, or any combination thereof. Such patients are often treated with a respiratory therapy system (e.g., a continuous positive airway pressure (CPAP) system) that delivers pressurized air to help prevent the airway from narrowing or collapsing during sleep. The respiratory therapy system can generate physiological data associated with a sleep session, which can then be used to determine sleep-related parameters and/or generate a report indicative of sleep quality. However, if a user does not use the respiratory therapy system (e.g., the user removes the user interface) during a portion of a sleep session, there are gaps in the physiological data generated for that sleep session. The present disclosure is directed to solving these problems and others.

According to some implementations of the present disclosure, a method includes receiving first physiological data associated with a sleep session of a user from a first sensor. The method also includes receiving second physiological data associated with the sleep session of the user from a second sensor. The method also includes determining a first set of sleep-related parameters associated with the sleep session of the user based at least in part on the first physiological data. The method also includes determining a second set of sleep-related parameters associated with the sleep session of the user based at least in part on the second physiological data. The method also includes calibrating the second sensor based at least in part on a comparison between the first set of sleep-related parameters and the second set of sleep-related parameters.

According to some implementations of the present disclosure, a system includes a respiratory device, a user interface, a sensor, a memory, and a control system. The respiratory device is configured to supply pressurized air and generate first physiological data during a sleep session associated with a user of the respiratory device. The user interface is coupled to the respiratory device via a conduit. The user interface is configured to engage with the user during the sleep session to assist in directing the supplied pressurized air to the user's airway. The sensor is configured to generate second physiological data during a sleep session associated with the user of the respiratory device. The memory stores machine-readable instructions. The control system includes one or more processors configured to execute the machine-readable instructions to analyze the first physiological data and determine a first set of sleep-related parameters of the user during the sleep session. The control system is further configured to analyze the second physiological data to determine a second set of sleep-related parameters of the user during the sleep session. The control system is further configured to calibrate the sensor based at least in part on a comparison of the first set of sleep-related parameters and the second set of sleep-related parameters.

According to some implementations of the present disclosure, a system includes a user interface, a breathing device, a sensor, a memory, and a control system. The user interface is a mask configured to engage a user during a sleep session. The breathing device is coupled to the user interface via a conduit. The breathing device is configured to generate first physiological data associated with the user during the sleep session when the user interface is engaged with the user. The sensor is configured to generate second physiological data associated with the user of the breathing device during the sleep session when the user interface is engaged with the user and when the user interface is not engaged with the user. The memory stores machine-readable instructions. The control system includes one or more processors configured to execute the machine-readable instructions to analyze the first physiological data, the second physiological data, or both, and determine a first set of sleep-related parameters of the user during a first portion of the sleep session during which the user interface is engaged with the user. The control system is further configured to analyze the second physiological data to determine a second set of sleep-related parameters for the user during a second portion of the sleep session when the user interface is not engaged with the user. The control system is further configured to generate a report associated with the first set of sleep-related parameters and associated with the second set of sleep-related parameters.

According to some implementations of the present disclosure, a method includes receiving respiratory data associated with a user during a sleep session when a user interface of a respiratory therapy system engages with the user. The method also includes receiving sensor data associated with the user during a sleep session when the user interface engages with the user and when the user interface does not engage with the user. The method also includes accumulating respiratory data including past respiratory data and current respiratory data, and sensor data including past sensor data and current sensor data. The method includes training a machine learning algorithm with the past respiratory data and the past sensor data, such that the machine learning algorithm is configured to (i) receive the current respiratory data and the current sensor data as input information, and (ii) determine as output information an expected activity level that the user will experience during a predetermined period of time.

According to some implementations of the present disclosure, a system includes a user interface, a respiratory device, a sensor, a memory, and a control system. The user interface is configured to engage a user during a sleep session. The respiratory device is coupled to the user interface via a conduit. The respiratory device is configured to generate respiratory data associated with the user during a sleep session when the user interface is engaged with the user. The sensor is configured to generate sensor data associated with the user of the respiratory device during a sleep session when the user interface is engaged with the user and when the user interface is not engaged with the user. The memory stores machine-readable instructions. The control system includes one or more processors configured to execute the machine-readable instructions to accumulate respiratory data, including historical respiratory data and current respiratory data, and sensor data, including historical sensor data and current sensor data. The control system is further configured to train a machine learning algorithm with the historical respiratory data and the historical sensor data, such that the machine learning algorithm is configured to (i) receive the current respiratory data and the current sensor data as input information, and (ii) determine as output information an expected activity level that the user will experience during a predetermined period of time.

The above summary is not intended to represent every embodiment or every aspect of the present invention. Further features and benefits of the present invention will be apparent from the detailed description and figures that follow.

FIG. 1 is a functional block diagram of a system for generating physiological data associated with a user during a sleep session, according to some implementations of the present disclosure. FIG. 2 is a perspective view of the system, user, and bed companion of FIG. 1 in accordance with some implementations of the present disclosure. FIG. 1 is a process flow diagram of a method for determining sleep-related parameters associated with a user during a sleep session according to some implementations of the present disclosure. FIG. 1 is a process flow diagram of a method for generating a report associated with a sleep session according to some implementations of the present disclosure. FIG. 1 is a process flow diagram of a method for training a machine learning algorithm based on historical respiratory device data and historical sensor data in accordance with some implementations of the present disclosure. 1 illustrates an example timeline of a sleep session, according to some implementations of the present disclosure. 4 illustrates an example hypnogram associated with the sleep session of FIG. 3 according to some implementations of the present disclosure.

While the present disclosure is susceptible to various modifications and alternative forms, specific implementations and embodiments of the present disclosure have been shown by way of example in the drawings and are described in detail herein. It is understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but that the disclosure covers all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.

Many people suffer from sleep-related and/or breathing disorders. Examples of sleep-related and/or breathing disorders include periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep-disordered breathing (SDB), obstructive sleep apnea (OSA), apnea, Cheyne-Stokes respiration (CSR), respiratory failure, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disorders (NMD), and chest wall disorders.

Obstructive sleep apnea (OSA) is a form of sleep-disordered breathing (SDB) characterized by events such as occlusion or blockage of the upper airway during sleep due to an abnormally small upper airway in combination with the normal loss of muscle tone in the area of the tongue, soft palate, and posterior oropharynx. More generally, apnea generally refers to pauses in breathing (obstructive sleep apnea) or cessation of respiratory function (often referred to as central apnea) caused by air blockage. During an obstructive sleep apnea episode, breathing typically stops for about 15 to about 30 seconds.

Other types of apnea include hypopnea, hyperpnea, and hypercapnia. Hypopnea is generally characterized by slow or shallow breathing caused by narrowing of the airway rather than airway obstruction. Hyperpnea is generally characterized by an increase in the depth and/or rate of breathing. Hypercapnia is generally characterized by a sudden or excessive amount of carbon dioxide in the bloodstream and is typically caused by insufficient breathing.

Cheyne-Stokes respiration (CSR) is another form of sleep-disordered breathing. CSR is a disorder of the patient's respiratory regulator that is followed by alternating periods of waxing and waning ventilation, known as CSR cycles. CSR is characterized by repeated deoxygenation and reaeration of arterial blood.

Obesity hyperventilation syndrome (OHS) is defined as the combination of severe obesity and chronic awake hypercapnia in the absence of any other clear cause of hypoventilation. Symptoms include dyspnea, morning headache, and excessive daytime sleepiness.

Chronic obstructive pulmonary disease (COPD) encompasses any of a group of diseases of the lower airways that share certain characteristics, such as increased resistance to air movement, a prolonged expiratory phase of breathing, and loss of normal elasticity of the lungs.

Neuromuscular disorders (NMD) encompass a multitude of diseases and illnesses that impair muscle function directly through intrinsic muscle pathology or indirectly through neuropathology. Chest wall disorders are a group of thoracic deformities that cause ineffective connections between the respiratory muscles and the rib cage.

These and other disorders are characterized by specific events that occur during sleep (e.g., snoring, apnea, hypopnea, restless legs, sleep disturbances, choking, rapid heart rate, dyspnea, asthma attacks, epileptic episodes, seizures, or any combination thereof).

Apnea-hypopnea index (AHI) is an index used to indicate the severity of apnea during a sleep session. AHI is determined by dividing the number of apnea and/or hypopnea events experienced by a user during a sleep session by the total number of hours of sleep in that sleep session. The events may be, for example, pauses in breathing lasting at least 10 seconds. An AHI of less than 5 is considered normal. An AHI between 5 and 15 is considered to indicate mild sleep apnea. An AHI between 15 and 30 is considered to indicate moderate sleep apnea. An AHI of 30 or greater is considered to indicate severe sleep apnea. In children, an AHI greater than 1 is considered abnormal. Sleep apnea can be considered "controlled" when the AHI is normal or when the AHI is normal or mild. AHI can also be used in combination with oxygen saturation to indicate the severity of obstructive sleep apnea.

With reference to FIG. 1, system 100 includes a control system 110, a respiratory therapy system 120, one or more sensors 130, and an external device 170. As described herein, system 100 can generally be used to generate a first set of physiological data associated with a user during a sleep session using respiratory system 120 and generate a second set of physiological data using one or more of sensors 130 that are external to respiratory system 120, which in turn can be analyzed to determine a first set of sleep-related parameters and a second set of sleep-related parameters.

The control system 110 includes one or more processors 112 (hereinafter processors 112). The control system 110 is generally used to control (e.g., operate) various components of the system 100 and/or analyze data acquired and/or generated by the components of the system 100. The processor 112 may be a general-purpose or special-purpose processor or microprocessor. Although one processor 112 is shown in FIG. 1, the control system 110 may include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors, etc.) that may be in a single housing or may be located remotely from one another. The control system 110 may be coupled to and/or located within, for example, the housing of the external device 170, a portion (e.g., a housing) of the respiratory system 120, and/or one or more of the housings of the sensor 130. The control system 110 may be centralized (in one such housing) or distributed (in two or more such housings that are physically separate). In such implementations that include two or more housings housing the control system 110, such housings can be located proximate to one another and/or remotely.

The memory device 114 stores machine-readable instructions executable by the processor 112 of the control system 110. The memory device 114 may be any suitable computer-readable storage device or medium, such as, for example, a random or serial access memory device, a hard drive, a solid state drive, a flash memory device, etc. Although one memory device 114 is shown in FIG. 1, the system 100 may include any suitable number of memory devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). The memory device 114 may be coupled to and/or located within the housing of the respiratory device 122, the housing of the external device 170, the housing of one or more of the sensors 130, or any combination thereof. As with the control system 110, the memory device 114 may be centralized (within one such housing) or distributed (within two or more physically distinct such housings).

In some implementations, the memory device 114 (FIG. 1) stores a user profile associated with the user. The user profile may include, for example, demographic information associated with the user, biometric information associated with the user, medical information associated with the user, self-reported user feedback, sleep parameters associated with the user (e.g., sleep-related parameters recorded from one or more previous sleep sessions), or any combination thereof. The demographic information may include, for example, information indicative of the user's age, the user's gender, the user's race, a family history of insomnia, the user's employment status, the user's education status, the user's socio-economic status, or any combination thereof. The medical information may include, for example, information indicative of one or more medical conditions associated with the user, the user's medication use, or both. The medical information data may further include a Multiple Sleep Latency Test (MSLT) test result or score and/or a Pittsburgh Sleep Quality Index (PSQI) score or value. The self-reported user feedback may include information indicative of a self-reported subjective sleep score (e.g., poor, fair, good, etc.), the user's self-reported subjective stress level, the user's self-reported subjective fatigue level, the user's self-reported subjective health status, recent life events experienced by the user, or any combination thereof.

The electronic interface 119 is configured to receive data (e.g., physiological data and/or audio data) from one or more sensors 130, which can be stored in the memory device 114 and/or analyzed by the processor 112 of the control system 110. The electronic interface 119 can communicate with the one or more sensors 130 using a wired or wireless connection (e.g., using an RF communication protocol, a WiFi communication protocol, a Bluetooth® communication protocol, a cellular network, etc.). The electronic interface 119 can include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. The electronic interface 119 can also include another processor and/or another memory device that is the same or similar to the processor 112 and memory device 114 described herein. In some implementations, the electronic interface 119 is coupled to or integrated with the external device 170. In other implementations, the electronic interface 119 is coupled to or integrated with (e.g., within the housing) the control system 110 and/or the memory device 114.

The respiratory system 120 (also referred to as a respiratory therapy system) includes a respiratory pressure therapy device 122 (also referred to herein as a respiratory device 122), a user interface 124, a conduit 126 (also referred to as a tube or air circuit), a display device 128, and a humidification tank 129. In some implementations, the control system 110, the memory device 114, the display device 128, one or more of the sensors 130, and the humidification tank 129 are part of the respiratory device 122. Respiratory pressure therapy refers to the application of an air supply to the entrance of a user's airway at a controlled target pressure that is nominally positive relative to the atmosphere throughout the user's breathing cycle (unlike negative pressure therapies such as tank ventilators and positive-negative pressure cuirass). The respiratory system 120 is generally used to treat individuals suffering from one or more sleep-related breathing disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea).

The breathing device 122 is generally used to generate pressurized air that is delivered to a user (e.g., using one or more motors driving one or more compressors). In some implementations, the breathing device 122 generates a continuous constant air pressure that is delivered to a user. In other implementations, the breathing device 122 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In still other embodiments, the breathing device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, the breathing device 122 can deliver at least about 6 cmH ₂ O, at least about 10 cmH 2 O, at least about 20 cmH ₂ O, about 6 cmH ₂ O to about 10 cmH ₂ O, about 7 cmH ₂ O to about 12 cmH ₂ O, etc. The breathing device 122 can also deliver pressurized air at a predetermined flow rate, for _example , between about -20 L/min and about 150 L/min, while maintaining a positive pressure (relative to ambient pressure).

The user interface 124 engages a portion of the user's face and delivers pressurized air from the breathing device 122 to the user's airways to help keep the airways from narrowing and/or obstructing while sleeping. This may also increase the user's oxygen intake while sleeping. Depending on the therapy being applied, the user interface 124 may form a seal with an area or portion of the user's face, for example, to facilitate delivery of gas at a pressure sufficiently different from ambient pressure to provide a therapeutic effect, such as a positive pressure of about 10 cm _H2O relative to ambient pressure. In other forms of therapy, such as oxygen delivery, the user interface may not include a sufficient seal to facilitate delivery of a gas supply to the airways at a positive pressure of about 10 cm _H2O .

As shown in FIG. 2, in some implementations, the user interface 124 is a face mask that covers the user's nose and mouth. Alternatively, the user interface 124 can be a nasal mask that provides air to the user's nose, or a nasal pillow mask that delivers air directly to the user's nostrils. The user interface 124 can include a number of straps (e.g., including hook-and-loop fasteners) for positioning and/or stabilizing the interface on a portion of the user (e.g., face) and a conformable cushion (e.g., silicone, plastic, foam, etc.) that helps provide an airtight seal between the user interface 124 and the user. The user interface 124 can also include one or more vents to allow carbon dioxide and other gases exhaled by the user 210 to escape. In other implementations, the user interface 124 is a mouthpiece (e.g., a night guard mouthpiece shaped to fit the user's teeth, a mandible repositioning device, etc.) for directing pressurized air into the user's mouth.

The conduit 126 (also referred to as an air circuit or tube) allows air to flow between two components of the respiratory system 120, such as the respiratory device 122 and the user interface 124. In some implementations, there may be separate branches of this conduit for inhalation and exhalation. In other implementations, a single branch conduit is used for both inhalation and exhalation.

One or more of the breathing device 122, the user interface 124, the conduit 126, the display device 128, and the humidification tank 129 may include one or more sensors (e.g., a pressure sensor, a flow sensor, or more generally, any of the other sensors 130 described herein). These one or more sensors may be used, for example, to measure the air pressure and/or flow rate of the pressurized air provided by the breathing device 122.

The display device 128 is generally used to display image(s), including still images, video, or both, and/or information about the respiratory device 122. For example, the display device 128 can provide information about the status of the respiratory device 122 (e.g., whether the respiratory device 122 is on or off, the pressure of the air being delivered by the respiratory device 122, the temperature of the air being delivered by the respiratory device 122, etc.) and/or other information (e.g., a sleep score (also referred to as myAir score), the current date/time, personal information of the user 210, etc.). In some implementations, the display device 128 serves as a human machine interface (HMI) that includes a graphic user interface (GUI) configured to display image(s) as an input interface. The display device 128 can be an LED display, an OLED display, a liquid crystal display, etc. The input interface can be, for example, a touch screen or a touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with the respiratory device 122.

The humidification tank 129 is coupled to or integrated with the respiratory device 122 and includes a water reservoir that can be used to humidify the pressurized air delivered from the respiratory device 122. The respiratory device 122 may include a heater that heats water in the humidification tank 129 to humidify the pressurized air provided to the user. Additionally, in some implementations, the conduit 126 may also include a heating element (e.g., coupled to and/or embedded in the conduit 126) that heats the pressurized air delivered to the user.

The respiratory system 120 may be used as a ventilator or a positive airway pressure (PAP) system, such as, for example, a continuous positive airway pressure (CPAP) system, an automatic positive airway pressure system (APAP), a biphasic or variable positive airway pressure system (BPAP or VPAP), or any combination thereof. A CPAP system delivers a predetermined air pressure (e.g., determined by a sleep physician) to a user. An APAP system automatically varies the air pressure delivered to a user, for example, based on breathing data associated with the user. A BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., inspiratory positive airway pressure or IPAP) and a second predetermined pressure (e.g., expiratory positive airway pressure or EPAP) that is lower than the first predetermined pressure.

2, a portion of the system 100 (FIG. 1) according to some implementations is depicted. A user 210 of the breathing system 120 and a bed partner 220 are in a bed 230 and lying on a mattress 232. A user interface 124 (e.g., a full face mask) may be worn by the user 210 during a sleep session. The user interface 124 is fluidly coupled and/or connected to the breathing device 122 via a conduit 126. The breathing device 122 delivers pressurized air to the user 210 via the conduit 126 and the user interface 124 to increase air pressure in the user's 210 throat and help prevent airway obstruction and/or narrowing during sleep. The breathing device 122 may be positioned on a nightstand 240 immediately adjacent to the bed 230, as shown in FIG. 2, or more generally on any surface or structure generally adjacent to the bed 230 and/or the user 210.

1, the one or more sensors 130 of the system 100 include a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, a radio frequency (RF) receiver 146, an RF transmitter 148, a camera 150, an infrared sensor 152, a photoelectric (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a moisture sensor 176, a LiDAR sensor 178, or any combination thereof. Generally, one or each of the sensors 130 is configured to output sensor data received and stored in the memory device 114 or one or more other memory devices.

Although the one or more sensors 130 are illustrated and described as including each of a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, an RF receiver 146, an RF transmitter 148, a camera 150, an infrared sensor 152, a photoelectric (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a moisture sensor 176, and a LiDAR sensor 178, it is more general that the one or more sensors 130 may include any combination and any number of each of the sensors described and/or illustrated herein.

The one or more sensors 130 may be used to generate, for example, physiological data, audio data, or both. The physiological data generated by one or more of the sensors 130 may be used by the control system 110 to determine a sleep/wake signal and one or more sleep-related parameters associated with the user during a sleep session. The sleep/wake signal may indicate one or more sleep states including wakefulness, relaxed wakefulness, micro-arousal, rapid eye movement (REM) stage, a first non-REM stage (often referred to as "N1"), a second non-REM stage (often referred to as "N2"), a third non-REM stage (often referred to as "N3"), or any combination thereof. The sleep/wake signal may also be provided with a timestamp indicating when the user went to bed, when the user woke up, when the user attempted to fall asleep, etc. The sleep/wake signal may be measured by the sensor(s) 130 at a predetermined sampling rate during the sleep session, such as, for example, one sample per second, one sample per 30 seconds, one sample per minute, etc. Examples of one or more sleep-related parameters that may be determined for a user during a sleep session based on the sleep/wake signal include total time in bed, total time asleep, sleep onset latency, wake-after parameters, sleep efficiency, fragmentation index, or any combination thereof.

Physiological and/or audio data generated by one or more sensors 130 may also be used to determine a respiratory signal associated with the user during a sleep session. The respiratory signal generally indicates the user's breathing or breathing during a sleep session. The respiratory signal may indicate, for example, respiratory rate, respiratory rate variability, inspiration amplitude, expiration amplitude, inspiration to expiration ratio, number of events per hour, event patterns, pressure settings of the respiratory device 122, or any combination thereof. The event(s) may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leak (e.g., from the user interface 124), restless legs, sleep disorder, choking, increased heart rate, dyspnea, asthma attack, epileptic episode, seizure, or any combination thereof.

The pressure sensor 132 outputs pressure data that can be stored in the memory device 114 and/or analyzed by the processor 112 of the control system 110. In some implementations, the pressure sensor 132 is an air pressure sensor (e.g., an atmospheric pressure sensor) that generates sensor data indicative of the breathing (e.g., inhalation and/or exhalation) of a user of the respiratory system 120 and/or the ambient pressure. In such implementations, the pressure sensor 132 can be coupled to or integrated with the respiratory device 122. The pressure sensor 132 can be, for example, a capacitive sensor, an electromagnetic sensor, a piezoelectric sensor, a strain gauge sensor, an optical sensor, a potentiometric sensor, or any combination thereof. In one example, the pressure sensor 132 can be used to determine the user's blood pressure.

The flow sensor 134 outputs flow data that can be stored in the memory device 114 and/or analyzed by the processor 112 of the control system 110. The flow sensor 134 is used to determine the airflow rate from the respiratory device 122, the airflow rate through the conduit 126, the airflow rate through the user interface 124, or any combination thereof. In such implementations, the flow sensor 134 can be coupled to or integrated with the respiratory device 122, the user interface 124, or the conduit 126. The flow sensor 134 can be, for example, a mass flow sensor such as a rotary flow meter (e.g., a Hall effect flow meter), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot wire sensor, a vortex sensor, a membrane sensor, or any combination thereof.

The temperature sensor 136 outputs temperature data that can be stored in the memory device 114 and/or analyzed by the processor 112 of the control system 110. In some implementations, the temperature sensor 136 generates temperature data indicative of the core body temperature of the user 210 (FIG. 2), the skin temperature of the user 210, the temperature of the air flowing from the respiratory device 122 and/or through the conduit 126, the temperature within the user interface 124, the ambient temperature, or any combination thereof. The temperature sensor 136 can be, for example, a thermocouple sensor, a thermistor sensor, a silicon band gap temperature sensor or semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

The microphone 140 outputs audio data that can be stored in the memory device 114 and/or analyzed by the processor 112 of the control system 110. The audio data generated by the microphone 140 can be reproduced as one or more sounds (e.g., sounds from the user 210) during a sleep session. The audio data from the microphone 140 can also be used to identify events experienced by the user during a sleep session (e.g., using the control system 110), as described in further detail herein. The microphone 140 can be coupled to or integrated with the breathing device 122, the usage interface 124, the conduit 126, or the external device 170.

The speaker 142 outputs sound waves that are audible to a user of the system 100 (e.g., user 210 of FIG. 2). The speaker 142 can be used, for example, as an alarm clock or to play alerts or messages to the user 210 (e.g., in response to an event). In some implementations, the speaker 142 can be used to communicate audio data generated by the microphone 140 to the user. The speaker 142 can be coupled to or integrated with the breathing device 122, the user interface 124, the conduit 126, or the external device 170.

The microphone 140 and the speaker 142 can be used as separate devices. In some implementations, the microphone 140 and the speaker 142 can be incorporated into an acoustic sensor 141, for example, as described in WO 2018/050913, which is incorporated herein by reference in its entirety. In such implementations, the speaker 142 generates or emits sound waves at predetermined intervals, and the microphone 140 detects the reflection of the sound waves emitted from the speaker 142. The sound waves generated or emitted by the speaker 142 have a frequency that is inaudible to the human ear (e.g., below 20 Hz or above about 18 kHz) so as not to disturb the sleep of the user 210 or the bedmate 220 (FIG. 2). Based at least in part on the data from the microphone 140 and/or the speaker 142, the control system 110 can determine the location of the user 210 (FIG. 2) and/or one or more of the sleep-related parameters described herein.

In some implementations, the sensor 130 includes (i) a first microphone that is the same as or similar to the microphone 140 and is integrated into the acoustic sensor 141, and (ii) a second microphone that is the same as or similar to the microphone 140, but separate and distinct from the first microphone that is integrated into the acoustic sensor 141.

The RF transmitter 148 generates and/or emits radio waves having a predetermined frequency and/or a predetermined amplitude (e.g., in a high frequency band, in a low frequency band, long wave signals, short wave signals, etc.). The RF receiver 146 detects reflections of the radio waves emitted from the RF transmitter 148, and this data can be analyzed by the control system 110 to determine the location of the user 210 (FIG. 2) and/or one or more of the sleep-related parameters described herein. The RF receiver (either the RF receiver 146 and the RF transmitter 148, or another RF pair) can also be used for wireless communication between the control system 110, the breathing device 122, one or more sensors 130, the external device 170, or any combination thereof. Although the RF receiver 146 and the RF transmitter 148 are shown as separate and distinct elements in FIG. 1, in some implementations the RF receiver 146 and the RF transmitter 148 are combined as part of the RF sensor 147. In some such implementations, the RF sensor 147 includes control circuitry. Specific forms of RF communication may include Wi-Fi and Bluetooth (registered trademark), etc.

In some implementations, the RF sensor 147 is part of a mesh system. One example of a mesh system is a WiFi mesh system, which may include mesh nodes, mesh router(s), and mesh gateway(s), each of which may be mobile/movable or fixed. In such implementations, the WiFi mesh system includes a WiFi router and/or a WiFi controller, and one or more satellites (e.g., access points), each of which includes an RF sensor that is the same as or similar to the RF sensor 147. The WiFi router and satellites constantly communicate with each other using WiFi signals. The WiFi mesh system can be used to generate motion data based on changes in the WiFi signal (e.g., differences in received signal strength) between the router and satellite(s) caused by objects or people moving and partially obstructing the signal. This motion data may indicate motion, breathing, heart rate, walking, falling, behavior, etc., or any combination thereof.

The camera 150 outputs image data that can be reproduced as one or more images (e.g., still images, video images, thermal images, or a combination thereof) that can be stored in the memory device 114. The image data from the camera 150 can be used by the control system 110 to determine one or more of the sleep-related parameters described herein. For example, the image data from the camera 150 can be used to determine the location of the user, determine the time when the user 210 enters the bed 230 (FIG. 2), and determine the time when the user 210 leaves the bed 230.

The infrared (IR) sensor 152 outputs infrared image data that can be reproduced as one or more infrared images (e.g., still images, video, or both) that can be stored in the memory device 114. The infrared data from the IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep session, including the temperature of the user 210 and/or the movement of the user 210. The IR sensor 152 can also be used in combination with the camera 150 in measuring the presence, location, and/or movement of the user 210. The IR sensor 152 can detect infrared light having a wavelength between about 700 nm and about 1 mm, for example, while the camera 150 can detect visible light having a wavelength between about 380 nm and about 740 nm.

The PPG sensor 154 outputs physiological data associated with the user 210 (FIG. 2) that can be used to determine one or more sleep-related parameters, such as, for example, heart rate, heart rate variability, cardiac cycle, respiratory rate, inspiration amplitude, expiration amplitude, inspiration-to-expiration ratio, estimated blood pressure parameter(s), or any combination thereof. The PPG sensor 154 can be worn by the user 210, embedded in clothing and/or fabric worn by the user 210, embedded in and/or coupled to the user interface 124 and/or its associated headgear (e.g., straps, etc.), etc.

The ECG sensor 156 outputs physiological data associated with electrical activity of the user's 210 heart. In some implementations, the ECG sensor 156 includes one or more electrodes positioned on or around a portion of the user 210 during a sleep session. The physiological data from the ECG sensor 156 can be used, for example, to determine one or more of the sleep-related parameters described herein.

The EEG sensor 158 outputs physiological data associated with electrical activity of the user's 210 brain. The EEG sensor 158 includes one or more electrodes that are positioned on or around the scalp of the user 210 during a sleep session. The physiological data from the EEG sensor 158 can be used, for example, to determine the sleep state of the user 210 at any given time during the sleep session. The EEG sensor 158 can be integrated into the user interface 124 and/or its associated headgear (e.g., straps, etc.).

The capacitance sensor 160, the force sensor 162, and the strain gauge sensor 164 output data that can be stored in the memory device 114 and that the control system 110 can use to determine one or more of the sleep-related parameters described herein. The EMG sensor 166 outputs physiological data associated with electrical activity produced by one or more muscles. The oxygen sensor 168 outputs oxygen data indicative of the oxygen concentration of the gas (e.g., in the conduit 126 or in the user interface 124). The oxygen sensor 168 can be, for example, an ultrasonic oxygen sensor, an electrical oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or any combination thereof. In some implementations, the one or more sensors 130 also include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a blood pressure sensor, an oximetry sensor, or any combination thereof.

The analyte sensor 174 can be used to detect the presence of an analyte in the exhaled breath of the user 210. Data output by the analyte sensor 174 can be stored in the memory device 114 and can be used by the control system 110 to determine the identity and concentration of any analytes in the breath of the user 210. In some implementations, the analyte sensor 174 is positioned near the mouth of the user 210 to detect analytes in the breath exhaled from the mouth of the user 210. For example, if the user interface 124 is a face mask that covers the nose and mouth of the user 210, the analyte sensor 174 can be positioned in the face mask to monitor the mouth breathing of the user 210. In other implementations, such as when the user interface 124 is a nasal mask or nasal pillow mask, the analyte sensor 174 can be positioned near the nose of the user 210 to detect analytes in the breath exhaled through the user's nose. In yet another implementation, the analyte sensor 174 may be positioned near the mouth of the user 210 when the user interface 124 is a nasal mask or a nasal pillow mask. In this implementation, the analyte sensor 174 may be used to detect whether air is inadvertently leaking from the mouth of the user 210. In some implementations, the analyte sensor 174 is a volatile organic compound (VOC) sensor that may be used to detect carbon-based chemicals or compounds. In some implementations, the analyte sensor 174 may also be used to detect whether the user 210 is breathing through the nose or mouth. For example, if the presence of an analyte is detected by data output by the analyte sensor 174 positioned near the mouth of the user 210 or in the face mask (in implementations where the user interface 124 is a face mask), the control system 110 may use this data as an indication that the user 210 is breathing through the mouth.

The moisture sensor 176 outputs data that can be stored in the memory device 114 and used by the control system 110. The moisture sensor 176 can be used to detect moisture in various areas surrounding the user (e.g., inside the conduit 126 or the user interface 124, near the face of the user 210, near the connection between the conduit 126 and the user interface 124, near the connection between the conduit 126 and the breathing device 122, etc.). Thus, in some implementations, the moisture sensor 176 can be coupled or integrated with the user interface 124 or integrated with the conduit 126 to monitor the humidity of the pressurized air from the breathing device 122. In other implementations, the moisture sensor 176 is located near any area where moisture levels need to be monitored. The moisture sensor 176 can also be used to monitor the humidity of the ambient environment surrounding the user 210, such as the air in a bedroom.

A Light Detection and Ranging (LiDAR) sensor 178 can be used for depth sensing. This type of optical sensor (e.g., a laser sensor) can be used to detect objects and create a three-dimensional (3D) map of the surrounding environment, such as a living space. LiDAR generally utilizes a pulsed laser to measure time of flight. LiDAR is also referred to as 3D laser scanning. In one use case of such a sensor, a fixed or mobile device (such as a smartphone) with a LiDAR sensor 166 can measure and map an area more than 5 meters away from the sensor. The LiDAR data can be fused with point cloud data estimated, for example, by an electromagnetic RADAR sensor. The LiDAR sensor(s) 178 can also use artificial intelligence (AI) to automatically create a geofence for the RADAR system by detecting and classifying features in the space that may pose a problem for the RADAR system, such as glass windows (which may be highly reflective to the RADAR). LiDAR can also be used to estimate a person's height, as well as changes in height that occur when a person sits down, falls, etc. LiDAR can be used to create a 3D mesh representation of the environment. In a further application, LiDAR can reflect off solid surfaces (e.g., radio-transparent materials) through which radio waves pass, allowing classification of different types of obstacles.

Although shown separately in FIG. 1, any combination of one or more sensors 130 may be integrated and/or coupled to any one or more of the components of the system 100, including the breathing device 122, the user interface 124, the conduit 126, the humidification tank 129, the control system 110, the external device 170, or any combination thereof. For example, the microphone 140 and the speaker 142 are integrated and/or coupled to the external device 170, and the pressure sensor 130 and/or the flow sensor 132 are integrated and/or coupled to the breathing device 122. In some implementations, at least one of the one or more sensors 130 is not coupled to the breathing device 122, the control system 110, or the external device 170, and is positioned generally adjacent to the user 210 during a sleep session (e.g., positioned on or in contact with a portion of the user 210, worn by the user 210, coupled or positioned on a nightstand, coupled to a mattress, coupled to a ceiling, etc.).

For example, as shown in FIG. 2, one or more of the sensors 130 may be located at a first location 250A on the nightstand 240 adjacent to the bed 230 and the user 210. Alternatively, one or more of the sensors 130 may be located at a second location 250B on and/or within the mattress 232 (e.g., the sensor is coupled and/or integrated into the mattress 232). Furthermore, one or more of the sensors 130 may be located at a third location 250C on the bed 230 (e.g., the secondary sensor(s) 140 are coupled and/or integrated into the headboard, footboard, or other location on the frame of the bed 230). One or more of the sensors 130 may also be located at a fourth location 250D on a wall or ceiling generally adjacent to the bed 230 and/or the user 210. One or more of the sensors 130 may be located at a fifth location 250E such that one or more of the sensors 130 are coupled to and/or positioned on and/or within the housing of the respiratory device 122 of the respiratory system 120. Additionally, one or more of the sensors 130 may be located at a sixth location 250F such that the sensor is coupled to and/or positioned on the user 210 (e.g., the sensor(s) are embedded or coupled to fabric or clothing worn by the user 210 during a sleep session). More generally, one or more of the sensors 130 may be located at any suitable location relative to the user 210 such that the sensor(s) 140 may generate physiological data associated with the user 210 and/or bed companion 220 during one or more sleep sessions.

1, the external device 170 includes a processor 172, a memory 174, and a display device 176. The external device 170 may be, for example, a mobile device such as a smartphone, a tablet, or a laptop. The processor 172 is the same as or similar to the processor 112 of the control system 110. Similarly, the memory 174 is the same as or similar to the memory device 114 of the control system 110. The display device 176 is generally used to display an image(s), including still images, moving images, or both. In some implementations, the display device 176 serves as a human machine interface (HMI) including a graphic user interface (GUI) configured to display the image(s) and an input interface. The display device 176 may be an LED display, an organic electroluminescence display, a liquid crystal display, or the like. The input interface may be, for example, a touch screen or a touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with the external device 170.

1 as separate and distinct components of the system 100, in some implementations the control system 110 and/or the memory device 114 are integrated into the external device 170 and/or the respiratory device 122. Alternatively, in some implementations the control system 110 or a portion thereof (e.g., the processor 112) may be located in the cloud (e.g., integrated into a server, integrated into an Internet of Things (IoT) device, connected to the cloud, subject to edge cloud processing, etc.), in one or more servers (e.g., remote servers, local servers, etc., or any combination thereof).

Although the system 100 is shown as including all of the above components, a system for generating physiological data and determining sleep-related parameters may include more or fewer components. For example, a first alternative system includes the control system 110, the respiratory system 120, and at least one of the one or more sensors 130. As another example, a second alternative system includes the respiratory system 120, more than one of the one or more sensors 130, and an external device 170. As a further alternative, a third alternative system includes the control system 110 and more than one of the one or more secondary sensors 140. Thus, any portion or portions of the components shown and described herein may be used and/or combined with one or more other components to form various systems for determining sleep-related parameters associated with a sleep session.

In many cases, a user of the respiratory system 120 will only wear the user interface 124 for a portion of a sleep session (e.g., 1 hour of an 8-hour sleep session, 2 hours of an 8-hour sleep session, 4 hours of an 8-hour sleep session, 6 hours of an 8-hour sleep session, etc.). For example, the user may first wear the mask of the user interface 124 to fall asleep, wake up and remove the user interface 124, and then fall asleep again. In other cases, the user may not wear the user interface 124 at all during a sleep session (e.g., the user may only use the respiratory system 120 every other day). If the user does not actively use the respiratory system 120, the sensor(s) in the respiratory system 120 will not be able to obtain physiological data (e.g., to determine sleep-related parameters or generate a report indicative of sleep quality). In other words, not using the respiratory system 120 for an entire sleep session creates gaps (or no physiological data at all) in the physiological data for that sleep session, leaving the user, his/her treating physician, and other interested parties (e.g., family members, bed companions, etc.) blind to the quality of sleep (or lack thereof) experienced when the user is not adhering to prescribed use of the respiratory system 120. Users typically experience poorer quality sleep when not using the respiratory system 120 than when using the system as prescribed. Determining and quantifying such differences in sleep quality is useful to encourage and/or motivate users to utilize the system and adhere to prescribed use.

The same or similar types of physiological data acquired using a sensor physically coupled to or integrated with the respiratory system 120 can also be generated or acquired using an independent external sensor (e.g., a sensor integrated into a mobile device that is separate and distinct from the respiratory system 120). This independent sensor has the advantage that it can be used to record physiological data during a sleep session even when the respiratory system 120 is not in use and/or is turned off. However, due to variability in sensor equipment, the physiological data acquired or generated by the sensor may differ from the physiological data acquired or generated by the respiratory therapy system. For example, the respiratory therapy system may use a pressure sensor and/or a flow sensor to determine the user's respiratory signal or respiration rate, while the external sensor may use a microphone to determine the respiration rate. Due to the variability in sensors, it is often difficult to accurately compare the physiological data acquired by the respiratory therapy system with the physiological data acquired by the independent sensor(s) to determine sleep-related parameters and/or generate a report indicative of sleep quality.

With reference to FIG. 3, a method 300 for determining sleep-related parameters of a user during a sleep session is illustrated. One or more of the steps of method 300 described herein may be implemented using system 100 (FIGS. 1 and 2).

Step 301 of method 300 includes generating or acquiring first physiological data associated with a user during a sleep session. The first physiological data is generated by a first one of one or more sensors 130 described herein, or a first group of one or more sensors 130. For example, step 301 may include generating or acquiring the first physiological data using an air pressure sensor 130 and/or a flow sensor 132 that are physically coupled to or integrated with the respiratory system 120 (FIG. 1). The first physiological data generated or acquired in step 301 using the first sensor may include any of the physiological data described herein. For example, the first physiological data may be derived from measurements of air pressure (e.g., using air pressure sensor 130), air flow (e.g., using flow sensor 132), or both within the respiratory device 122, the user interface 124, the conduit 126 (FIG. 1), or any combination thereof.

Step 302 of method 300 includes generating or acquiring second physiological data associated with the user during the sleep session. For example, step 302 may include generating or acquiring the second physiological data using a second one or a second set of one or more sensors 130 that are separate and distinct from the first sensor (step 301). The second physiological data generated or acquired during step 302 using the second sensor may be a different type of physiological data than the first physiological data (e.g., the first physiological data is derived from pressure and/or airflow and the second physiological data is derived from measurements of the user's motion or sound). Generally, the generation of the second physiological data during step 302 and the generation of the first physiological data during step 301 are substantially simultaneous, such that the first physiological data (step 301) and the second physiological data (step 302) can be compared over at least a portion of the time during the sleep session (when the user is using the respiratory system 120).

Unlike the first sensor (e.g., pressure sensor 132 and/or flow sensor 134 coupled or integrated with respiratory system 120) used during step 301 to generate the first physiological data, in some implementations, the sensor(s) used during step 302 to generate or obtain the second physiological data are not coupled or integrated with respiratory system 120. That is, the second sensor(s) used for the second physiological data in step 302 are separate and distinct from respiratory system 120. As described herein, the second sensor(s) can be positioned generally adjacent to the user 210 as an independent standalone sensor (e.g., positioned or coupled to the bed 230, mattress 232, nightstand 240, ceiling, etc.) or integrated or coupled to the external device 170. Alternatively, the second sensor(s) used during step 302 can be coupled to the housing of the respiratory device 122 of respiratory system 120. In some such alternative implementations, the second sensor(s) used during performance of step 302 does not measure the pressure or flow of the pressurized air generated by the respiratory system 120.

Step 303 of method 300 (FIG. 3) includes analyzing first physiological data to determine a first set of sleep-related parameters of the user during a sleep session (step 301). For example, the first physiological data (step 301) can be stored in memory device 114 of control system 110 (FIG. 1), and machine-readable instructions stored in memory device 114 can be executed by processor 112 to analyze the first physiological data. The first set of sleep-related parameters can include, for example, a first sleep score, a first flow signal, a first respiratory signal, a first respiratory rate, a first inspiration amplitude, a first expiration amplitude, a first inspiration-to-expiration ratio, a first number of events per hour, a first average number of events per hour, a first event pattern, a first sleep state, a first pressure setting of respiratory device 122, a first heart rate, a first heart rate variability, a first movement of user 210, or any combination thereof.

Step 304 of method 300 (FIG. 3) includes analyzing the second physiological data (step 302) to determine a second set of sleep-related parameters of the user during the sleep session. For example, the second physiological data (step 302) can be stored in memory device 114 of control system 110, and machine-readable instructions stored in memory device 114 can be executed by processor 112 (FIG. 1) to analyze the second physiological data. The second set of sleep-related parameters can include, for example, a second sleep score, a second flow signal, a second respiratory signal, a second respiratory rate, a second inhalation amplitude, a second exhalation amplitude, a second inhalation-to-exhalation ratio, a second number of events per hour, a second average number of events per hour, a second event pattern, a second sleep state, a second pressure setting of respiratory device 122, a second heart rate, a second heart rate variability, a second movement of user 210, or any combination thereof. Steps 303 and 304 of method 300 can be performed substantially simultaneously or sequentially (e.g., step 303 is completed before step 304 is started, step 304 is completed before step 303 is started, etc.).

Step 305 of method 300 includes calibrating a second sensor(s) used to generate the second physiological data based at least in part on the first set of sleep-related parameters (step 303) and/or the second set of sleep-related parameters (step 304). Specifically, step 305 includes comparing the first set of sleep-related parameters (step 303) and the second set of sleep-related parameters (step 304). As described herein, because the first sensor(s) (e.g., pressure sensor 132 and/or flow sensor 134) are different from the second sensor(s), there may be variability between the first physiological data (step 301) and the second physiological data (step 302), which results in variability between the determined first set of sleep-related parameters (step 303) and the determined second set of sleep-related parameters (step 304) at any given time during the sleep session. For example, prior to calibrating the second sensor(s), the first set of sleep-related parameters (step 303) may include a first event pattern, a first average number of events per hour, a first respiratory signal, or any combination thereof, and the second set of sleep-related parameters (step 304) may include a second event pattern, a second average number of events per hour, a second respiratory signal, or any combination thereof that does not match one or more of the first event pattern, the first average number of events, or the first respiratory signal. As another example, noise in the data from the second sensor(s) (step 302) may cause the control system 110 to determine that the user has experienced or is experiencing one of the events described herein based on an analysis of the second physiological data, even if the user has not experienced or is actually experiencing the event(s) (e.g., based on an analysis of the first physiological data).

In some implementations, step 305 includes modifying one or more parameters of the sensor(s) based at least in part on a comparison between the first set of sleep-related parameters (step 303) and the second set of sleep-related parameters (step 304). Modifying one or more parameters of the second sensor(s) affects the second physiological data generated by the second sensor(s) during performance of step 302. For example, modifying these parameters may help remove outlying data points and/or other noise in the recorded data. The analysis and/or modification of the one or more parameters may include linear and nonlinear transform operations, resampling, template matching, autocorrelation and cross-correlation, morphological processing, and the like. Additionally, in some implementations, the analysis and/or modification of the one or more parameters may include characterization and adjustment followed by recharacterization. Such characterization and adjustment may involve reading physiological and/or other signals, or injecting a known signal that can be recognized by both sensors (e.g., the first sensor and the secondary sensor(s)) and against which the various sensors can be referenced. The one or more sensor parameters of the second sensor(s) that can be altered include the frequency, phase, power, amplitude, intensity, modulation, beam pattern, turning one or more antennas of the sensor on or off, beam forming, the physical location of one or more antennas of the sensor, the physical location of the sensor, the spectral shape, or any combination thereof. In some implementations, the one or more sensor parameters are automatically altered by the control system 110. In other implementations, the control system 110 causes instructions or other cues to be displayed (e.g., using the display device 176 of the external device 170, the display device 128 of the CPAP system 120, or both) to prompt the user to alter the one or more parameters (e.g., physically reposition the sensor).

In some implementations, step 305 includes modifying one or more parameters of the machine-readable instructions executed by the processor 112 of the control system 110 (FIG. 1) when analyzing the second physiological data during step 304 based at least in part on a comparison between the first set of sleep-related parameters (step 303) and the second set of sleep-related parameters (step 304). Modifying the parameters of the machine-readable instructions adjusts how the second set of sleep-related parameters are determined during step 304. For example, if the sleep-related parameters are determined based on at least two types of physiological data (e.g., motion data from the motion sensor 138 and image data from the camera 150), the parameters of the machine-readable instructions stored in the memory device 114 can be modified to weight each type of physiological data differently. In some implementations, step 305 includes modifying one or more parameters of the second sensor and modifying one or more parameters of the machine-readable instructions stored in the memory device 114 of the control system 110.

Steps 301-305 of method 300 may be repeated one or more times until one or more of the parameters in the first set of sleep-related parameters (step 303) match, substantially match, or correspond to one or more of the parameters in the second set of sleep-related parameters (step 304) (e.g., the second set of sleep-related parameters are within a predetermined standard deviation from the first set of sleep-related parameters). For example, in a first iteration of method 300 in which the first set of sleep-related parameters (step 303) do not match the second set of sleep-related parameters (step 304), the secondary sensor(s) 140 are calibrated during step 305 (e.g., by changing one or more parameters of the sensor, changing one or more parameters of the machine-readable instructions, or both). Steps 301-304 may be repeated in a second iteration of method 300. If the first set of sleep-related parameters (step 303) still does not sufficiently match the second set of sleep-related parameters (step 304) during the second iteration, step 305 includes recalibrating the second sensor(s) by changing the same parameters that were changed during the first iteration of method 300 or by changing various parameters and/or machine-readable instructions of the second sensor(s), or both. In this manner, method 300 can be repeated multiple times (e.g., 5, 20, 50, 200, 1000, etc.) at predetermined intervals (e.g., every 0.01 seconds, 0.1 seconds, 2 seconds, 10 seconds, 60 seconds, 5 minutes, 30 minutes, 60 minutes, etc.) until the calibration performed during step 305 causes the first set of sleep-related parameters (step 303) to match or correspond to the second set of sleep-related parameters (step 304) in a subsequent iteration or iterations.

In some implementations, the method 300 includes calibrating the secondary sensor(s) 140 using a machine learning algorithm during step 305. In such implementations, information indicative of (i) the change(s) and/or machine-readable instructions to one or more parameters of the second sensor(s) and (ii) the comparison between the first set of sleep-related parameters (step 303) and the second set of sleep-related parameters (step 304) is stored in the memory device 114 (FIG. 1) of the control system 110. This information can be used by the machine learning algorithm over the course of multiple iterations of the method 300 to, for example, aid in calibrating the second sensor(s) by determining which change or combination of changes to the one or more parameters have a corresponding effect on the difference between the first set of sleep-related parameters (step 303) and the second set of sleep-related parameters (step 304). If the machine learning algorithm determines that a particular sensor or machine-readable instruction parameter or parameter change does not affect the difference between the set of sleep-related parameters, then the parameter is not changed in subsequent iterations of method 300. Thus, the use of the machine learning algorithm can reduce the number of iterations of method 300 (parameter changes during execution of step 305) required to calibrate the second sensor(s).

Once the second sensor(s) have been calibrated using method 300, system 100 can continue to determine the second sleep-related parameters (step 304) even if the respiratory system 120 (FIG. 1) including the first sensor(s) is no longer generating the first set of physiological data (step 301), e.g., via pressure sensor 132 and/or flow sensor 134. Advantageously, because the second sensor(s) have been calibrated using method 300, the second set of sleep-related parameters continues to provide information indicative of a sleep session that is the same or similar to the first set of sleep-related parameters obtained using respiratory system 120. For example, if user 210 (FIG. 2) wears user interface 124 during a first portion of a sleep session, but then removes user interface 124 during a second portion of the sleep session, system 100 will continue to determine the sleep-related parameters of user 210, even though the respiratory system 120 was not used by user 210 throughout the entire sleep session.

With reference to FIG. 4, a method 400 for generating a report associated with a sleep session is illustrated. One or more of the steps of method 400 described herein may be implemented using system 100 (FIG. 1).

Step 401 of method 400 includes generating or acquiring first physiological data associated with a user during a sleep session when a user interface (e.g., user interface 124) is engaged with the user. Step 401 is similar to step 301 (FIG. 3) of method 300 in that the first physiological data may be acquired using one or more first sensors (e.g., pressure sensor 132 and/or flow sensor 134 integrated and/or coupled to respiratory device 122). Step 401 differs from step 301 (FIG. 3) in that the first physiological data is generated or acquired only when the user interface 124 of the respiratory system 120 is engaged with the user 210 (FIG. 2) during the sleep session. Thus, the first physiological data is not generated or acquired during portions of the sleep session when the user interface 124 is not engaged with the user 210.

In some implementations, step 401 includes determining whether the user interface 124 is engaged with the user 210 using a sensor(s) that is the same or different from the sensor(s) used to generate the first physiological data (step 401) or the second physiological data (step 402). For example, if the pressure sensor 132 and/or the flow sensor 134 detect a lack of air pressure and/or air flow to the user interface 124, this may indicate that the user interface 124 is not engaged with the user 210 (FIG. 2). As another example, the mask 124 may include one or more sensors that can be used to determine whether the user interface 124 is engaged with the user 210. In a further example, image data from the camera 150 (FIG. 1) may be analyzed using object recognition algorithms, facial recognition algorithms, or both to determine whether the user interface 124 is engaged with the user 210.

Step 402 of method 400 includes generating or acquiring second physiological data associated with the user during a sleep session when the user interface is engaged with the user and when the user interface is not engaged with the user. Step 402 is similar to step 302 (FIG. 3) of method 300 in that the second physiological data can be acquired by at least one of the sensors 130 of system 100 (FIG. 1) that is separate and distinct from the sensor(s) coupled or integrated with respiratory system 120 (e.g., a sensor coupled or integrated with external device 170). In addition to using a different sensor that is not integrated with system 120, step 402 also differs from step 401 in that the second physiological data is acquired regardless of whether the user interface 124 is engaged with user 210 (FIG. 2) during all or a portion of the sleep session.

Step 403 of method 400 includes analyzing the first physiological data (step 401), the second physiological data (step 402), or both to determine a first set of sleep-related parameters during a first portion of a sleep session during which a user interface is engaged with a user. For example, the first physiological data (step 401) and/or the second physiological data (step 402) can be stored in memory device 114 of control system 110 (FIG. 1), and machine-readable instructions stored in memory device 114 are executed by processor 112 to analyze the first and/or second physiological data. The first set of sleep-related parameters determined during step 403 are generally the same as or similar to the first set of sleep-related parameters in step 303 of method 300 (FIG. 3) described herein, except that the first set of sleep-related parameters determined during step 403 is limited to a first portion of the sleep session during which user interface 124 is engaged with user 210 (FIG. 2).

Step 404 of method 400 includes analyzing the second physiological data (step 402) to determine a second set of sleep-related parameters during a second portion of the sleep session during which the user interface is not engaged by the user. For example, the second physiological data (step 402) can be stored in memory device 114 of control system 110 (FIG. 1), and machine-readable instructions stored in memory device 114 are executed by processor 112 to analyze the second physiological data. Because the first set of sleep-related parameters is limited to the first portion of the sleep session during which the user interface is not engaged, the first physiological data is not used to determine the second set of sleep-related parameters during performance of step 404.

The first portion of the sleep session (step 403) and/or the second portion of the sleep session (step 404) may include a combination of contiguous or non-contiguous times during the sleep session. For example, if the user 210 (FIG. 2) wears the user interface 124 for the first three hours of the sleep session, removes the user interface 124 for the next two hours of the sleep session, and wears the user interface 124 again for the last two hours of the sleep session (for a total of seven hours), the first portion includes five hours and the second portion includes two hours. In some implementations, the first portion or the second portion includes the entire sleep session (e.g., the user 210 wears the user interface 124 for the entire sleep session or does not wear the user interface 124 at all for the entire sleep session).

Step 405 of method 400 includes generating a report associated with the first set of sleep-related parameters (step 403) and the second set of sleep-related parameters (step 404). For example, memory device 114 (FIG. 1) of control system 110 may include machine-readable instructions executable by processor 112 to analyze the first set of sleep-related parameters (step 403) and the second set of sleep-related parameters (step 404) and generate a report. The generated sleep session report may be stored in memory device 114 of control system 110, displayed on display device 176 of external device 170 and/or display device 128 of respiratory system 120 after the sleep session (e.g., when the user awakens), transmitted to a third party (e.g., a treating physician, a CPAP device technician), or any combination thereof. By storing the generated reports in memory device 114, system 100 can compare reports from multiple sleep sessions to determine trends and provide recommendations and/or forecasts, as further described herein.

In some implementations, the report generated during step 405 may include a comparison of at least a portion of the first set of sleep-related parameters (step 403) with at least a portion of the second set of sleep-related parameters (step 404). In other implementations, the report may indicate the user's 210 sleep quality during a first portion of the sleep session in which the user interface 124 is engaged with the user 210 and the user's 210 sleep quality during a second portion of the sleep session in which the user interface 124 is not engaged with the user. As described herein, the user typically experiences poorer sleep quality when the user interface 124 is not engaged compared to the sleep quality when the user interface 124 is engaged. The report may quantify and communicate the difference in sleep quality to the user to encourage compliance with the prescribed use of the respiratory system 120 or indicate when the prescribed use of the respiratory system 120 is no longer necessary.

For example, the report may include a sleep score or metric indicative of the quality of sleep during the first and second portions of the sleep session. In general, the sleep score may be represented as a number (e.g., an integer) between 0 and 100, with a score of 100 indicating good quality sleep, a score of 0 indicating poor quality sleep, and scores of 1 to 99 representing various qualities in between. The sleep score may be determined based on, for example, the number or type of events during the sleep session, the duration of the sleep session, the duration of one or more sleep states during the sleep session (e.g., the relative duration of REM and/or non-REM sleep), the amount of movement of the user during the sleep session, any of the other sleep-related parameters and/or events described herein, or a combination thereof. The sleep score may also be displayed or communicated to the user 210 in a manner indicative of how use or non-use of the respiratory system 120 has affected the user's sleep score.

In some implementations, the report generated during step 405 may include a sleep adherence score criterion for the user 210 based on the prescribed usage of the respiratory system 120. Information indicative of the prescribed usage of the respiratory system 120 may be stored in the memory device 114 (FIG. 1) of the control system 110. The sleep adherence score criterion indicates how well the user 210 adhered to the prescribed usage during the sleep session. The sleep adherence score criterion may be expressed using a number (e.g., an integer) between 0 and 100, a percentage, or other indicator (e.g., poor, fair, good, excellent, etc.). For example, if the user 210 is to wear the mask 124 for at least 80% of the sleep session and the user wears the user interface 124 for 50% of the sleep session, the sleep adherence score criterion may be expressed as 62.5. The sleep adherence score criterion may be useful in motivating or encouraging the user 210 to adhere to the prescribed usage of the respiratory system 120 and/or in providing information to a treating physician. Further details regarding the sleep adherence score criteria (e.g., treatment quality index) are described in Application No. 15,520,663, filed April 20, 2017, and published as US2017/0311879 on November 2, 2017, which is incorporated by reference in its entirety.

As noted above, in some implementations, the generated report includes a sleep score or metric indicative of sleep quality. In such implementations, the generated report may also include recommendations for adjustments to one or more of the above sleep habits to help improve the sleep quality score. For example, the recommendations may indicate to the user to change the user's bedtime and increase the duration of the sleep session to increase the sleep score. The report may further include a predicted quantitative improvement in the sleep quality score or metric corresponding to the user making the recommended adjustments to the one or more sleep habits. For example, if the sleep score of the report indicates poor quality sleep, the report may recommend increasing the sleep session duration (e.g., from 5 hours to 7 hours) and increasing the amount of time the user wears the mask 124 (e.g., from 50% of the sleep session to at least 90%), and predict a quantitative improvement in the sleep score (e.g., an increase from a score of 50 to a score of 90). The quantitative prediction may be determined, for example, based on past generated reports stored in the memory device 114 of the control system 110.

In some implementations, this predicted quantitative improvement in sleep quality score or criterion can be determined even if the user 210 has not yet used the CPAP system 120. After an individual is diagnosed with a sleep-related breathing disorder and prescribed a respiratory therapy system, it often takes weeks, and sometimes months, for the individual to obtain the respiratory therapy system. In some cases, the individual may not be followed up during this delay period, or may change his or her mind about using the respiratory therapy system (e.g., the user may see an image of the respiratory therapy system and find it too intrusive or difficult to use). By using this sensor (e.g., integrated into the external device 170) to generate second physiological data during the sleep session, the system 100 can determine a sleep score and predict a quantitative improvement in the sleep quality score if the user 210 uses the respiratory system 120 to help stimulate, encourage, or motivate the user 210 to follow the prescription and obtain and use the respiratory system 120.

In some implementations, the report generated during step 405 may include recommendations regarding the use of the respiratory device 122 (FIG. 1) of the respiratory system 120 and/or recommendations regarding adjustments to the user's sleep habits. For example, the generated report may include recommendations to change the user's bedtime, the user's wake-up time, the duration of the sleep session, the time during the sleep session that the user wears the user interface 124 (e.g., at least 33% of the sleep session duration, at least 66% of the sleep session, at least 75% of the sleep session, 90% of the sleep session, etc.), or any combination thereof. Recommendations regarding the use of the respiratory device 122 of the CPAP system 120 may also include recommendations to use a different respiratory system (e.g., a CPAP system capable of delivering higher pressures) or different respiratory system components (e.g., a different user interface type).

The report generated during step 405 may also be used to ascertain whether the respiratory system 120 is improving sleep quality based on a comparison between the first set of sleep-related parameters (step 303) and the second set of sleep-related parameters (step 304). For example, if the report indicates that there is little difference between the first set of sleep-related parameters (step 303) and the second set of sleep-related parameters (step 304), this may indicate that the user 210 no longer requires the respiratory system 120. The comparison in the report may also be used to help identify other factors (e.g., undiagnosed health conditions, environmental conditions, etc.) that may be adversely affecting the user's sleep but that are not being treated by the respiratory system 120.

Step 406 of method 400 (FIG. 4) includes modifying one or more parameters of the respiratory device 122 of the respiratory system 120 based on the first physiological data (step 401), the second physiological data (step 402), the generated report (step 405), or any combination thereof. Step 406 may include, for example, modifying a ramp time of the respiratory device 122, modifying a pressure setting during a sleep session, modifying a pressure setting upon determining that the user has awakened from a sleep session, or any combination thereof.

Step 407 of method 400 is the same as or similar to step 305 of method 300 (FIG. 3) and includes calibrating a second sensor(s) used to generate or acquire the second physiological data during performance of step 402. Step 407 may include modifying one or more parameters of the second sensor(s), modifying one or more parameters of the machine-readable instructions stored in memory device 114 and executed by processor 112 of control system 110, or both.

Although method 400 has been described as obtaining first and second physiological data (steps 401 and 402) associated with user 210 (FIG. 2), in some implementations method 400 may also include generating or obtaining third physiological data associated with bed companion 220 (FIG. 2) during a sleep session. In such implementations, one of one of one of one of one of one of one of one of one of one or more of secondary sensors 140 and/or respiratory system 120 may be used to generate or obtain third physiological data of bed companion 220 that may be stored in memory device 114 of control system 110. The sensor used to generate or obtain the third physiological data may be the same sensor or a different sensor as the sensor used to generate or obtain the second physiological data during execution of step 402.

In implementations that include third physiological data associated with the bedmate 220, the report generated during step 405 may include a sleep quality score or metric indicative of the bedmate's 220 quality of sleep during the sleep session. The bedmate 220 may have a lower sleep score than the user 210 of the breathing system 120, even if the bedmate 220 does not suffer from a sleep-related breathing disorder. For example, the user 210's snoring may disrupt the bedmate's 220 sleep. As another example, air leakage from the user interface 124 worn by the user 210 may cause noise(s) that may disrupt the bedmate's 220 sleep. As such, the report generated during step 405 may include recommendations for adjustments to one or more sleep habits of the user 210 of the breathing system 120 aimed at helping improve the bedmate's 220 sleep quality metric. For example, the report may include a recommendation to increase or decrease the average time/hours of use of the breathing device 122 by the user 210 per sleep session. The report may further include a predicted quantitative improvement in bedmate's 220 sleep quality metrics in response to user 210 implementing the recommended adjustments to one or more sleep habits.

In some implementations, the generated report may also include recommendations for the user interface 124 to be worn by the user 210. For example, the report may recommend a different mask size or a different mask type (e.g., nasal mask, full mask, cradle mask, etc.) to help improve the fit or interface between the mask and the user 210.

Referring to FIG. 5, a method 500 for training a machine learning algorithm is shown. One or more of the steps of method 500 described herein can be performed using system 100 (FIG. 1).

Step 501 of method 500 includes using a respiratory device to generate or acquire respiratory data associated with a user during a sleep session when the user interface is engaged with the user. Step 501 is similar to step 401 (FIG. 4) of method 400 in that the first physiological data can be acquired using one of one or more secondary sensors 130 (FIG. 1) of system 100 that are integrated with the respiratory device 122 of the respiratory system 120. For example, when the user interface 124 is not engaged with the user 210 (FIG. 2), the pressure sensor 132 and/or the airflow sensor 134 in the respiratory device 122 do not generate or acquire the first physiological data. In some implementations, similar to step 401 of method 400 (FIG. 4), step 501 includes determining whether the user interface 124 is engaged with the user 210.

Step 502 of method 500 includes generating or acquiring sensor data associated with the user during a sleep session when the user interface is engaged with the user and when the user interface is not engaged with the user. Step 502 is similar to step 402 (FIG. 4) of method 400 in that the second physiological data can be acquired by a different one or more of the sensors 130 of the system 100 (FIG. 1) that are separate and distinct from the sensor(s) coupled or integrated with the respiratory system 120 (e.g., a sensor coupled or integrated with the external device 170).

Step 503 of method 500 includes accumulating respiratory data and sensor data. The accumulated respiratory data includes first physiological data currently being generated or acquired during execution of step 501 (hereinafter, current respiratory data) and first physiological data previously recorded from a previous iteration of method 500 (hereinafter, past respiratory data). Similarly, the accumulated sensor data includes second physiological data currently being generated or acquired during execution of step 502 (hereinafter, current sensor data) and second physiological data previously recorded from a previous iteration of method 500 (hereinafter, past sensor data). The past respiratory data and past sensor data may be generated over multiple sleep sessions and may be stored in memory device 114 of control system 110 (FIG. 1). The past respiratory data and/or past sensor data may be automatically deleted after being stored in memory device 114 indefinitely or for a predetermined period of time (e.g., one week, one month, six months, one year, three years, etc.).

Step 504 of method 500 includes training a machine learning algorithm (MLA) using the historical respiratory data and the historical sensor data accumulated during step 503. The MLA is trained to receive the current respiratory data (step 501) and the current sensor data (step 502) as input information and determine an expected activity level, an expected reaction time, an expected subjective sleepiness sleep, or any combination thereof as output information. That is, the MLA is trained using the historical respiratory data and the historical sensor data as a training data set. The historical respiratory data and the historical sensor data can be continually accumulated or updated (step 503) to update the training data set of the MLA. The MLA can be, for example, a deep learning algorithm or a neural network and can be stored as machine-readable instructions in the memory device 114 of the control system 110 executable by the processor 112.

Step 505 of method 500 includes determining an expected activity level that the user will experience during a predetermined period of time following the sleep session. After the MLA has been trained during step 504 using past respiratory and sensor data, the MLA can receive current respiratory data (step 501) and/or current sensor data as input information (step 502) and determine an expected activity level for the predetermined period of time as output information. The predetermined period of time can be about 0.1 hours to about 24 hours after the end of the sleep session, about 3 hours to about 16 hours after the sleep session, about 8 hours to about 14 hours after the sleep session, about 12 hours after the sleep session, about 24 hours to about 1 week after the sleep session, about 12 hours to about 1 month after the sleep session, etc.

Compliance by the user 210 with prescribed usage of the respiratory therapy system typically results in improved quality of sleep, increased activity throughout the day (e.g., number of steps), reduced resting heart rate, reduced heart rate variability, weight loss (e.g., due to increased calorie burn), improved diet (e.g., reduced calorie intake), reduced number of headaches, or any combination thereof. The predicted activity level determined during step 505 using the trained MLA (step 504) may include predicted number of steps, predicted number of calories burned, predicted resting heart rate, predicted heart rate variability, predicted number of headaches, predicted weight loss, or any combination thereof, during a predetermined period of time (e.g., 12 hours after the end of the sleep session). The predicted activity level determined during step 505 may be displayed or communicated to the user 210 using the display device 176 of the mobile device, the display device 128 of the respiratory system 120, or both. Communicating the determined predicted activity level to the user 210 can help encourage and motivate the user 210 to adhere to prescribed usage of the respiratory system 120.

Step 506 of method 500 includes using the trained MLA (step 504) to determine during this predetermined period an expected reaction time that the user will experience after the sleep session to a standardized test. Individuals who have had good quality sleep tend to be more alert and less sleepy than individuals who have had poor quality sleep. Reaction times measure or assess how quickly an individual responds to a stimulus using standardized tests, such as, for example, a click reaction time test, a tap reaction time test, a speed test, a recognition test, a solving test, a processing test, a decoding test, a stick reaction test, a flashlight reaction test, or any combination thereof. The expected reaction times determined during step 506 may be displayed or communicated to the user 210 using the display device 176 of the mobile device, the display device 128 of the respiratory system 120, or both. Communicating the determined expected reaction times to the user 210 may assist in encouraging or motivating the user 210 to adhere to the prescribed usage of the respiratory system 120.

Step 507 of method 500 includes using the trained MLA to determine an expected subjective sleepiness score that the user will experience after the sleep session (step 504). The subjective sleepiness score is based on the user's 210 subjective sensation after the sleep session and may be expressed as a number (e.g., an integer) between 0 and 100, or may be expressed using other indicators (e.g., extremely or very sleepy, moderately sleepy, neutral, not sleepy, etc.). The expected subjective sleepiness score determined during step 507 may be displayed or communicated to the user 210 using the display device 176 of the mobile device, the display device 128 of the respiratory system 120, or both. Communicating the expected subjective sleepiness score to the user 210 may help encourage or motivate the user 210 to adhere to prescribed usage of the respiratory system 120.

Step 508 of method 500 includes displaying a prompt to the user requesting subject feedback regarding the user's subjective sensations. The user's subjective sensations may be represented, for example, using descriptive terms (e.g., poor, fair, nervous, good, very good, sleepy, tired, rested, alert, etc.), numerical values (e.g., integers from 0 to 10, with 10 indicating the best subjective sensation and 0 indicating the worst subjective sensation), or both. The prompt may be displayed using the display device 176 of the external device 170 (FIG. 1), the display device 128 of the respiratory system 120, or both. The prompt generally instructs the user 210 to provide subjective feedback and may be visually displayed or communicated to the user 210 as alphanumeric text and/or other indicators. Alternatively, the prompt may be audibly communicated to the user 210 (e.g., using a speaker coupled to or integrated with the external device 170 that is the same as or similar to the speaker 148).

Similarly, subject feedback from user 210 can be received by display device 176 of external device 170. For example, the requested subject feedback can be provided by selecting (e.g., by clicking or tapping) one or more indicia displayed on display device 176, entering alphanumeric text (e.g., using a touch keyboard displayed on display device 176), using voice input (e.g., using a microphone coupled to or integrated with external device 170 that is the same as or similar to microphone 146), or using any combination thereof.

The MLA described herein may receive this prompt and train during step 504 using the subject feedback provided during step 508. The subject feedback provided over multiple sleep sessions may be stored in memory device 114 of control system 110 (FIG. 1) as past subject feedback for training the MLA in the same or similar manner as the past respiratory data and past sensor data described above. The MLA may be trained to determine a quality of life criterion (e.g., expressed as a numerical value or a descriptive sentence) that is a combination of the sleep scores or criteria described herein and the subject feedback provided during step 508.

Although methods 300, 400, and 500 have been described herein as collecting physiological data during a sleep session, the methods may more generally be implemented (e.g., using system 100) to generate or obtain the same or similar physiological data when the user is awake during the day. In such implementations, a sensor for generating the physiological data may be coupled to or integrated with external device 170, which may be located or located generally adjacent to user 210 (e.g., in user's pocket) throughout at least a portion of the day. Physiological data of the user generated or obtained outside of a sleep session may be compared to physiological data generated or obtained during a sleep session or to previously recorded physiological data generated or obtained outside of a sleep session. These comparisons can be used, for example, during step 504 of method 500 (FIG. 5), to train an MLA to predict activity levels (step 505), predict reaction time values (step 506), predict subjective sleep scores (step 507), or any combination thereof.

As used herein, a sleep session can be defined in a number of ways, for example, based on an initial start and end time. Referring to FIG. 6, an example timeline 600 of a sleep session is shown. The timeline 600 includes a time to go to bed (t _bed ), a time to fall asleep (t _GTS ), a time to first fall asleep (t _sleep ), a first micro-awakening MA ₁ and a second micro-awakening MA ₂ , a wake time (t _wake ), and a wake time (t _rise ).

A sleep session can be defined in multiple ways herein. For example, a sleep session can be defined by an initial start time and end time. In some implementations, a sleep session is the duration during which the user is asleep, i.e., the sleep session has a start time and an end time, during which the user does not wake up until the end time. That is, the time during which the user is awake is not included in the sleep session. From the first definition of a sleep session, if the user wakes up and falls asleep multiple times in one night, each sleep period separated by those awake periods is a sleep session.

Alternatively, in some implementations, a sleep session has a start time and an end time, during which the user can be awakened without the sleep session ending as long as the continuous time the user is awake is less than the wakefulness duration threshold. The wakefulness duration threshold can be defined as a percentage of the sleep session. The wakefulness duration threshold can be, for example, about 20 percent of the sleep session, about 15 percent of the sleep session duration, about 10 percent of the sleep session duration, about 5 percent of the sleep session duration, about 2 percent of the sleep session duration, etc., or any other threshold percentage. In some implementations, the wakefulness duration threshold is defined as, for example, about 1 hour, about 30 minutes, about 15 minutes, about 10 minutes, about 5 minutes, about 2 minutes, etc., or any other amount of time.

In some implementations, a sleep session is defined as the total time from the time the user first went to bed in the evening to the time the user last woke up the following morning. In other words, a sleep session can be defined as the time that begins at a first time (e.g., 10:00 PM) on a first date (e.g., Monday, September 7, 2020), referred to as the current evening, when the user first went to bed with the intention to sleep (as opposed to the user first intending to watch TV or play with their smartphone before going to sleep), and ends at a second time (e.g., 7:00 AM) on a second date (e.g., Tuesday, September 8, 2020), referred to as the following morning, when the user first woke up with the intention not to sleep again the following morning.

In some implementations, a user may manually define the start of a sleep session and/or manually end a sleep session. For example, a user may select (e.g., by clicking or tapping) one or more user-selectable elements displayed on the display device 172 of the external device 170 (FIG. 1) to manually start or end a sleep session.

6, the bed time t _bed is associated with the time when the user first gets into bed (e.g., bed 230 in FIG. 2) (e.g., when the user lies or sits in bed) before falling asleep. The bed time t _bed can be determined based on a bed threshold duration to distinguish between the time when the user gets into bed to sleep and the time when the user gets into bed for other reasons (e.g., to watch television). For example, the bed threshold duration can be at least about 10 minutes, at least about 20 minutes, at least about 30 minutes, at least about 45 minutes, at least about 1 hour, at least about 2 hours, etc. Although the bed time t _bed is described herein with reference to a bed, more generally the bed time t _bed can represent the time when the user first gets into some location (e.g., couch, chair, sleeping bag, etc.) to sleep.

The time to sleep (GTS) is associated with the time (t _bed ) at which the user first attempts to fall asleep after going to bed. For example, the user may engage in one or more activities (e.g., reading, watching television, listening to music, using external device 170, etc.) to relax after going to bed and before attempting to sleep. The time to first sleep (t _sleep ) is the time at which the user first falls asleep. For example, the time to first sleep (t _sleep ) may be the time at which the user first enters a non-REM sleep stage.

The wake time t _wake is a time associated with when the user wakes up without returning to sleep (e.g., instead of the user waking up in the middle of the night and returning to sleep). After the user first falls asleep, the user may experience one of many unconscious micro-awakenings (e.g., micro-awakenings MA ₁ and MA ₂ ) having a short duration (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.). The user goes through each of the micro-awakenings MA ₁ and MA ₂ before falling asleep again, rather than the wake time t _wake . Similarly, the user may have one or more conscious awakenings (e.g., waking up to go to the bathroom, taking care of children or pets, sleepwalking, etc.) after the user first falls asleep. However, the user returns to sleep after the awakening A. Thus, the wake time t _wake can be defined, for example, based on an awakening threshold duration (e.g., the user is awake for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.).

Similarly, the wake-up time t _rise is associated with the time when the user leaves the bed with the intention of ending the sleep session (e.g., not the user going to the bathroom in the middle of the night, caring for a child or pet, sleepwalking, etc.). In other words, the wake-up time t _rise is the time when the user last leaves the bed without returning to the bed until the next sleep session (e.g., the following night). Thus, the wake-up time t _rise can be defined, for example, based on a wake-up threshold duration (e.g., the user has been out of bed for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.). The second bed time t _bed after the sleep session can also be defined based on a wake-up threshold duration (e.g., the user has been out of bed for at least 4 hours, at least 6 hours, at least 8 hours, at least 12 hours, etc.).

As described above, the user may wake up and get out of bed one more time during the night between the first t _bed and the final t _rise . In some implementations, the final wake time t _wake and/or the final wake time t _rise are identified or determined based on a predetermined threshold duration after an event (e.g., falling asleep or getting out of bed). Such threshold duration can be customized for the user. For a typical user who goes to bed at night and wakes and gets up in the morning, any time between about 12 hours and about 18 hours (from when the user wakes up (t _wake ) or gets up (t _rise ) until they go to bed (t _bed ), fall asleep (t _GTS ) or go to sleep (t _sleep )) can be used. For users who spend a lot of time in bed, a shorter threshold time (e.g., about 8 hours to about 14 hours) can be used. This threshold time can be initially selected and/or later adjusted based on the system monitoring the user's sleep behavior.

The total time in bed (TIB) is the duration from the time t _bed to the time t _rise . The total sleep time (TST) is associated with the duration from the time of first falling asleep to the time of waking, excluding conscious or unconscious awakenings and/or microawakenings in between. The total sleep time (TST) is generally shorter than the total time in bed (TIB) (e.g., 1 minute shorter, 10 minutes shorter, 1 hour shorter, etc.). For example, referring to the timeline 600 of FIG. 6, the total sleep time (TST) is from the time of first falling asleep t _sleep to the time of waking t _wake , but excluding the durations of the first microawakening MA ₁ , the second microawakening MA ₂ , and the awakening A. As shown, in this example, the total sleep time (TST) is shorter than the total time in bed (TIB).

In some implementations, total sleep time (TST) can be defined as total sustained sleep time (PTST). In such embodiments, the total sustained sleep time excludes a predetermined initial portion or time of the first non-REM stage (e.g., light sleep stage). For example, the predetermined initial portion can be about 30 seconds to about 20 minutes, about 1 minute to about 10 minutes, about 3 minutes to about 5 minutes, etc. The total sustained sleep time is a measure of sustained sleep and smooths the sleep/wake hypnogram. For example, when a user first falls asleep, the user may enter the first non-REM stage for a very short time (e.g., about 30 seconds), return to the wake stage for a short time (e.g., 1 minute), and then return to the first non-REM stage. In this example, the total sustained sleep time excludes the first instance of the first non-REM stage (e.g., about 30 seconds).

In some implementations, a sleep session is defined as the time that begins at a time to go to bed (t _bed ) and ends at a time to wake up (t _rise ), i.e., the total time in bed (TIB). In some implementations, a sleep session is defined as the time that begins at a time to first fall asleep (t _sleep ) and ends at a time to wake up (t _wake ). In some implementations, a sleep session is defined as the total time to sleep (TST). In some implementations, a sleep session is defined as the time that begins at a time to go to sleep (t _GTS ) and ends at a time to wake up (t _wake ). In some implementations, a sleep session is defined as the time that begins at a time to go to bed (t _bed ) and ends at a time to wake up (t _wake ). In some implementations, a sleep session is defined as the time that begins at a time to go to bed (t _bed ) and ends at a time to wake up (t _wake ). In some implementations, a sleep session is defined as beginning at the first time to fall asleep (t _sleep ) and ending at the time to wake up (t _rise ).

Referring to FIG. 7, an exemplary hypnogram 700 corresponding to time series 600 (FIG. 6) is depicted according to some implementations. As shown, hypnogram 700 includes a sleep/wake signal 701, a wake stage axis 710, a REM stage axis 720, a light sleep stage axis 730, and a deep sleep stage axis 740. The intersection of sleep/wake signal 701 with one of axes 710-740 indicates the sleep stage at any given time during the sleep session.

The sleep/wake signal 701 may be generated based on physiological data associated with the user (e.g., generated by one or more of the sensors 130 described herein). The sleep/wake signal may indicate one or more sleep states including wakefulness, relaxed wakefulness, micro-arousal, REM stage, first non-REM stage, second non-REM stage, third non-REM stage, or any combination thereof. In some implementations, one or more of the first non-REM stage, second non-REM stage, and third non-REM stage may be lumped together and classified as a light sleep stage or a deep sleep stage. For example, the light sleep stage may include the first non-REM sleep stage, and the deep sleep stage may include the second non-REM sleep stage and the third non-REM sleep stage. Although the hypnogram 700 is shown in FIG. 7 as including a light sleep stage axis 730 and a deep sleep stage axis 740, the hypnogram 700 may include axes representing each of a first NREM stage, a second NREM stage, and a third NREM stage. In other implementations, the sleep/wake signal may also represent a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-to-expiration ratio, an event count per hour, an event pattern, or any combination thereof. Information describing the sleep/wake signal may be stored in the memory device 114.

The hypnogram 700 can be used to determine one or more sleep-related parameters, such as, for example, sleep onset latency (SOL), wake after sleep onset (WASO), sleep efficiency (SE), sleep fragmentation index, sleep blocks, or any combination thereof.

Sleep onset latency (SOL) is defined as the time between the time of sleep onset (t _GTS ) and the time of first sleep onset (t _sleep ). In other words, sleep onset latency indicates the time it takes from the first attempt of a user to fall asleep to actually falling asleep. In some implementations, sleep onset latency is defined as persistent sleep onset latency (PSOL). The persistent sleep onset latency differs from sleep onset latency in that it is defined as the duration from the time of sleep onset to a predetermined amount of persistent sleep. In some implementations, the predetermined amount of persistent sleep may include, for example, at least 10 minutes of sleep in the second non-REM stage, the third non-REM stage, and/or in the REM stage with 2 minutes or less of wakefulness, the first non-REM stage, and/or transitions therebetween. In other words, the sustained sleep onset latency requires, for example, up to 8 minutes of sustained sleep within the second NREM stage, the third NREM stage, and/or the REM stage. In other implementations, the predetermined amount of sustained sleep may include at least 10 minutes of sleep within the first NREM stage, the second NREM stage, the third NREM stage, and/or the REM stage after the initial sleep onset time. In such implementations, the predetermined amount of sustained sleep may exclude any microarousals (e.g., after 10 seconds of microarousals, the 10 minutes are not resumed).

A wake-on-the-spot (WASO) is associated with the total duration that the user is awake between the time of first falling asleep and the time of awakening. Thus, a wake-on-the-spot (WASO) includes brief awakenings and microawakenings during a sleep session, whether conscious or unconscious (e.g., microawakenings _MA1 and _MA2 shown in FIG. 4). In some implementations, a wake-on-the-spot (WASO) is defined as a persistent wake-on-the-spot (PWASO) that includes only total awakening durations having a predetermined length (e.g., more than 10 seconds, more than 30 seconds, more than 60 seconds, more than about 5 minutes, more than about 10 minutes, etc.).

Sleep efficiency (SE) is determined as the ratio of total time in bed (TIB) to total time asleep (TST). For example, if the total time in bed is 8 hours and the total sleep time is 7.5 hours, the sleep efficiency for the sleep session is 93.75%. Sleep efficiency represents the sleep hygiene of the user. For example, if the user goes to bed and spends time on other activities (e.g., watching television) before going to sleep, the sleep efficiency is reduced (e.g., the user is penalized). In some implementations, the sleep efficiency (SE) can be calculated based on the total time in bed (TIB) and the total time the user attempts to fall asleep. In such implementations, the total duration of the user's attempts to fall asleep is defined as the time from the time of sleep onset (GTS) to the time of wake-up as described herein. For example, if the total sleep time is 8 hours (e.g., from 11:00 p.m. to 7:00 a.m.), the time of fall asleep is 10:45 p.m., and the time of wake-up is 7:15 a.m., the sleep efficiency parameter is calculated to be about 94% in such an embodiment.

The fragmentation index is determined based at least in part on the number of awakenings during a sleep session. For example, if a user had two microawakenings (e.g., microawakenings MA ₁ and MA ₂ as shown in FIG. 7 ), the fragmentation index may be expressed as 2. In some implementations, the fragmentation index is scaled between a range of integers (e.g., between 0 and 10).

Sleep blocks are associated with transitions between any sleep stage (e.g., a first non-REM stage, a second non-REM stage, a third non-REM stage, and/or REM) and a wake stage. Sleep blocks can be calculated, for example, with a resolution of 30 seconds.

In some implementations, the systems and methods described herein may include generating or analyzing a hypnogram including a sleep/wake signal to determine or identify a time to bed (t _bed ), a time to fall asleep (t _GTS ), a time to first fall asleep (t _sleep ), one or more first microawakenings (e.g., MA ₁ and MA ₂ ), a time to wake (t _wake ), a time to wake (t _rise ), or any combination thereof based at least in part on the sleep/wake signal of the hypnogram.

In other implementations, one or more of the sensors 130 can be used to determine or identify a time to go to bed (t _bed ), a time to fall asleep (t _GTS ), a time to first go to sleep (t _sleep ), one or more first micro-awakenings (e.g., MA ₁ and MA ₂ ), a time to wake up (t _wake ), a time to rise (t _rise ), or any combination thereof, and thus define a sleep session. For example, a time to go to bed t _bed can be determined based on data generated by the motion sensor 138, the microphone 140, the camera 150, or any combination thereof, etc. The time of sleep onset may be determined based on, for example, data from motion sensor 138 (e.g., data indicating no movement by the user), data from camera 150 (e.g., data indicating no movement by the user and/or data indicating the user has turned off the lights), data from microphone 140 (e.g., data indicating the user has turned off the television), data from external device 170 (e.g., data indicating the user is no longer using external device 170), data from pressure sensor 132 and/or flow sensor 134 (e.g., data indicating the user has turned on respiratory device 122, data indicating the user has donned user interface 124, etc.), or any combination thereof.

One or more elements or aspects or steps or portions thereof from any one or more of claims 1 to 84 below may be combined with one or more elements or aspects or steps or portions thereof from any one or more of the other claims 1 to 84 or combinations thereof to form one or more further implementations and/or claims of the disclosure.

Although the present disclosure has been described with reference to one or more particular embodiments or implementations, those skilled in the art will recognize that numerous modifications may be made without departing from the spirit and scope of the present disclosure. Each of these implementations and obvious variations thereof are contemplated as falling within the spirit and scope of the present disclosure. And, it is also contemplated that further implementations according to various aspects of the present disclosure may combine any number of features from any of the implementations described herein.

Claims (32)

A method implemented by one or more computers, comprising:
receiving first physiological data from a first sensor associated with a sleep session of a user;
receiving second physiological data from a second sensor associated with the user's sleep session;
determining a first set of sleep-related parameters associated with the sleep session of the user based at least in part on the first physiological data;
determining a second set of sleep-related parameters associated with the sleep session of the user based at least in part on the second physiological data;
calibrating the second sensor based at least in part on a comparison between the first set of sleep-related parameters and the second set of sleep-related parameters, where prior to calibrating the second sensor, the second set of sleep-related parameters are inconsistent with the first set of sleep-related parameters and, after calibrating the second sensor, the second set of sleep-related parameters are consistent with the first set of sleep-related parameters;
operating the first sensor and the calibrated second sensor, determining a sleep-related parameter from the first physiological data received from the first sensor when the first sensor is operating, and determining a sleep-related parameter from the second physiological data received from the second sensor when the first sensor is not operating during a subsequent sleep session. The method of claim 1, wherein the first sensor is physically coupled to or integrated with a respiratory therapy system. The method of claim 2, wherein the respiratory therapy system includes a respiratory device configured to supply pressurized air and a user interface coupled to the respiratory device via a conduit. The method of claim 2 or claim 3, wherein the first sensor is a pressure sensor or a flow sensor. 3. The method of claim 2, wherein the first sensor is configured to generate the first physiological data when the user interface is engaged with the user, and the second sensor is configured to generate the second physiological data when the user interface is engaged with the user and when the user interface is not engaged with the user. The method of any one of claims 1 to 5, wherein the second sensor is a motion sensor, an acoustic sensor, an RF sensor, a PPG sensor, or any combination thereof. The method of any one of claims 1 to 6, wherein calibrating the second sensor includes modifying one or more parameters of the second sensor. 8. The method of claim 7, wherein the one or more parameters of the sensor include a frequency, a phase, a power, an amplitude, an intensity, a modulation of the signal of the sensor, a beam pattern of one or more antennas of the sensor, on and off, a beam forming of one or more antennas of the sensor, a physical location, a physical location of the sensor, or any combination thereof. determining the second set of sleep-related parameters is performed by a control system executing machine-readable instructions stored in a memory;
The method of claim 1 , wherein calibrating the second sensor comprises altering one or more parameters of the machine-readable instructions. The method of any one of claims 1 to 9, wherein the first set of sleep-related parameters includes a first respiratory signal associated with the user during the sleep session, and the second set of sleep-related parameters includes a second respiratory signal associated with the user during the sleep session. 11. The method of claim 10, wherein, prior to the calibration of the second sensor, the second respiratory signal does not match the first respiratory signal, and, after the calibration of the second sensor, the second respiratory signal matches the first respiratory signal. The method of any one of claims 2 to 11, wherein the second sensor is physically coupled to or integrated with a user device that is separate and distinct from the respiratory therapy system. 3. The method of claim 2, further comprising communicating to the user an indication of the first set of sleep-related parameters, the second set of sleep-related parameters, or both. 14. The method of claim 13, wherein the indication includes a comparison of at least a portion of the first set of sleep-related parameters to at least a portion of the second set of sleep-related parameters. 15. The method of claim 14, wherein the indication indicates the user's quality of sleep during a first portion of the sleep session in which the user interface is engaged with the user and the user's quality of sleep during a second portion of the sleep session in which the user interface is not engaged with the user. The method of claim 13 or 14, further comprising communicating to the user a recommendation to adjust one or more sleep habits. 17. The method of claim 16, wherein the recommendation to adjust one or more sleep habits of the user includes a recommendation to change the time the user goes to bed, the time the user wakes up, the duration of the sleep session, the amount of time the user wears the user interface during the sleep session, or any combination thereof. The method of any one of claims 14 to 17, wherein the indication includes a sleep quality metric indicative of the user's quality of sleep during the sleep session and a recommendation to adjust one or more sleep habits of the user to assist in improving the sleep quality metric. 19. The method of claim 18, wherein the indication further comprises a predicted quantitative improvement in the sleep quality metric corresponding to the user implementing the recommendation to adjust the one or more sleep habits. 20. The method of claim 19, wherein the recommendation to adjust the one or more sleep habits includes a recommendation to increase an average duration of use of a respiratory therapy system. receiving third physiological data associated with a bed partner of the user during the sleep session;
determining a third set of sleep-related parameters associated with the bedmate during the sleep session;
21. The method of claim 1, further comprising communicating a manifestation of at least one of the third set of sleep-related parameters to the user, a bed partner, or both. 22. The method of claim 21, wherein the indication further comprises a recommendation to adjust one or more sleep habits of the user to assist in improving at least one of the third set of sleep-related parameters of the bedmate. 23. The method of claim 22, wherein the indication further comprises a predicted quantitative improvement in at least one of the third set of sleep-related parameters of the bedmate responsive to the user's implementation of the recommendation to adjust the one or more sleep habits of the user. 24. The method of any one of claims 2 to 23, further comprising altering one or more parameters of a respiratory therapy system based at least in part on the first set of sleep-related parameters, the second set of sleep-related parameters, or both. 25. The method of claim 24, wherein altering the one or more parameters includes altering a ramp time of a respiratory device of the respiratory therapy system, altering a pressure setting of the respiratory device, altering a pressure setting upon determining that the user has awakened from the sleep session, or any combination thereof. 26. The method of any one of claims 1 to 25, wherein the first set of sleep-related parameters comprises a sleep score, a flow signal, a respiration signal, a respiration rate, an inhalation amplitude, an exhalation amplitude, an inhalation to exhalation ratio, a number of events per hour, an event pattern, a sleep state, a pressure setting of a respiratory device of a respiratory therapy system, a heart rate, a heart rate variability, a movement of the user, or any combination thereof. 27. The method of any one of claims 1 to 26, wherein the second set of sleep related parameters comprises a sleep score, a respiratory signal, a respiratory rate, an inhalation amplitude, an exhalation amplitude, an inhalation to exhalation ratio, a number of events per hour, an event pattern, a sleep state, a heart rate, a heart rate variability, a movement of the user, or any combination thereof. 28. The method of claim 26 or 27, wherein the event pattern comprises central apnea, obstructive apnea, mixed apnea, hypopnea, snoring, periodic limb movements, restless legs syndrome, or any combination thereof. a control system comprising one or more processors;
a memory having machine-readable instructions stored thereon;
A system, wherein the control system is coupled to the memory and the machine-readable instructions stored in the memory are executed by at least one of the one or more processors of the control system to perform the method of any one of claims 1 to 28. A system for communicating one or more indications to the user, the system comprising a control system configured to implement the method of any one of claims 1 to 28. A computer program product comprising instructions which, when executed by one or more computers, cause the one or more computers to perform the method of any one of claims 1 to 28. The computer program product of claim 31, wherein the computer program product is a non-transitory computer-readable medium.